def test_net_comparison(self):
# (a + b) * (c + d) == a * c + a * d + b * c + b * d
net1 = core.Net("net1")
a, b, c, d = net1.AddExternalInputs("a", "b", "c", "d")
a_b = net1.Sum([a, b], "a+b")
c_d = net1.Sum([c, d], "c+d")
x = net1.Mul([a_b, c_d], "x")
net2 = core.Net("net2")
ac = net2.Mul([a, c], "ac")
ad = net2.Mul([a, d], "ad")
bc = net2.Mul([b, c], "bc")
bd = net2.Mul([b, d], "bd")
y = net2.Sum([ac, ad, bc, bd], "y")
input_values = {
blob: np.array([i], dtype=np.float32)
for i, blob in enumerate([a, b, c, d])
}
NetGradientChecker.CompareNets(
[net1, net2],
[[x], [y]],
[0],
inputs_with_grads=[a, b, c, d],
input_values=input_values,
)
def test_net_gradient_checker(self):
model = model_helper.ModelHelper(name="test")
const = model.net.AddExternalInputs("const1", "const2")
fc = brew.fc(model, dim_in=3, dim_out=4, blob_in="X", blob_out="Y", axis=0)
dist = [model.net.SquaredL2Distance([fc, c]) for c in const]
losses = [model.net.AveragedLoss(d) for d in dist] # using two losses here
workspace.RunNetOnce(model.param_init_net)
NetGradientChecker.Check(
model.net,
outputs_with_grad=losses,
input_values={"X": np.array([1, 2, 3], dtype="float32"),
const[0]: np.array([1, 1, 1, 1], dtype="float32"),
const[1]: np.array([2, 2, 2, 2], dtype="float32")},
input_to_check="X",
)
